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Connections between Computer Science and Biology. connections. bioinformatics: computational approach to problems in molecular biology biological processes inspire algorithms and data structures in computer science biomolecules “compute” biological organisms “compute”. bioinformatics.

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Connections l.jpg
connections

  • bioinformatics: computational approach to problems in molecular biology

  • biological processes inspire algorithms and data structures in computer science

  • biomolecules “compute”

  • biological organisms “compute”


Bioinformatics l.jpg
bioinformatics

  • sequencing the genome

  • predicting the structure of molecules

  • predicting genes, molecular function

  • constructing evolutionary trees

  • modeling cellular networks

  • ...


Constructing evolutionary trees l.jpg
constructing evolutionary trees

“The affinities of all the beings of the same class have sometimes been represented by a great tree. I believe this simile largely speaks the truth. The green and budding twigs may represent existing species; and those produced during each former year may represent the long succession of extinct species.” - Darwin, Origin of the Species


Constructing evolutionary trees5 l.jpg
constructing evolutionary trees

  • traditional approach: use morphological features of organisms (number of legs, etc.)

  • current approach: use base sequences of universal molecules such as RNA


Rna molecules l.jpg
RNA molecules

  • strings of ribo-nucleic acids, of which there are four types, denoted by A, C, G, U.

    5’ - ACCAUGGAC - 3’

  • some “universal” RNA molecules function in life’s most basic processes, and so mutate slowly



Two possible evolutionary trees l.jpg

CAGG

Aardvark

CAGA

Bison

CGCG

Chimp

UGCA

Dog

UGCG

Elephant

two possible evolutionary trees

UGCG

CACG

  • which is a better fit with the data? why?

UGCG

CACG

CAGG

UGCG

UGCG

CAGG

CAGG

Aardvark

CAGA

Bison

CGCG

Chimp

UGCA

Dog

UGCG

Elephant


Parsimony score l.jpg
parsimony score

  • to get a parsimony score for a tree, count the number of places where a nucleotide differs from a parent to a child


Parsimony problem l.jpg
parsimony problem

  • input: RNA sequences for some taxa, or species

  • output: the most parsimonious tree for the input taxa

    the more taxa, the more possible trees that are candidates for being the output


Application of parsimony luo et al nature jan 2001 l.jpg
application of parsimony(Luo et al., Nature, Jan 2001)

  • did mammals evolve independently on the north and south continents?


How many trees are there l.jpg
how many trees are there?

  • unfortunately, the number of possible trees grows exponentially with the number of taxa (organisms)

  • example of an exponential function: 2n (2 multiplied n times)

  • if there are n taxa, there are even more than 2n possible evolutionary trees



Complexity of the parsimony problem l.jpg
complexity of the parsimony problem

  • all known algorithms for exactly solving the parsimony problem require an exponential number of steps - this is a so-called NP-hard problem

  • in practice, heuristic algorithms are typically used, which try to search in an intelligent way for a good tree, but offer no guarantee of finding the best tree


Connections biologically inspired data structures l.jpg
connections: biologically inspired data structures

  • tree structures for organizing data are ubiquitous in computing (e.g. folders in a windows environment)

  • programming language environments support operations on trees (add-node, find-parent, etc.) for the programmer to use


Summary l.jpg
summary

  • strong connections between biology and cs

  • many computational problems, such as constructing parsimonious evolutionary trees, are “intractable”

  • algorithms for intractable problems are often heuristic


Vocabulary l.jpg
vocabulary

  • bioinformatics

  • evolutionary tree construction; parsimony problem

  • exponential running time, intractable problem (technically sometimes called NP-hard problem)

  • heuristic algorithms


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